Graph-Based Representation Learning for Identifying Fraud in Transaction Networks

This study proposes a graph neural network (GNN)-based method for fraud detection in financial transaction networks. By deeply modeling the transaction graph structure and the relationships between nodes, the method enhances the ability to identify complex fraudulent behaviors. First, financial transaction data is transformed into a graph structure, where transaction accounts are represented as nodes and the flow of funds as edges. Node features such as transaction amount and timestamp are used for node representation learning. Next, graph neural network architectures, such as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), are employed to uncover the latent associations and interactions between nodes. This helps identify anomalous fraudulent transactions. To improve the model's generalization and robustness, the study also introduces contrastive learning strategies and imbalance handling techniques. A weighted loss function is used to optimize the model's performance on minority fraud samples. Experimental results show that the proposed method outperforms traditional machine learning models and other deep learning approaches across various evaluation metrics on publicly available datasets. Notably, it achieves a better balance between precision and recall. This method effectively combines graph structural information with deep learning techniques, providing a novel solution for intelligent financial risk control.